Multi-robot Information Fusion: Considering spatial uncertainty models

The work presented in this thesis covers the topic of deployment for mobile robot teams. By connecting robots in teams they can perform a better job than each individual is capable of. It also gives redundancy, increases robustness, provides scalability, and increases efficiency. Multi-robot Information Fusion also results in a broader perspective for decision making. This thesis focuses on methods for estimating formation and trajectories and how these can be used for deployment of a robot team…


1 Introduction
1.1 Background
1.1.1 Why Multiple Robots?
1.1.2 Teams vs. Groups of Robots
1.1.3 Localization and Mapping
1.1.4 Formation and Deployment
1.2 The Grand Vision
1.3 The Structure of the Thesis
2 Framework
2.1 Stochastic Mapping
2.1.1 Kalman Filter
2.1.2 Map Representation
2.1.3 Adding Information
2.1.4 Moving Robots
2.2 Smoothing and Mapping
2.2.1 Representation
2.2.2 Adding Information
2.2.3 Collaborative Smoothing and Mapping
2.2.4 Base-node Estimate
3 Modeling of Perception
3.1 Observation Model
3.2 Observation Prediction
3.2.1 Observation between Two Poses
3.3 Characteristics for Common Sensors
3.3.1 Laser Scanner
3.3.2 Camera
3.3.3 Stereo Camera
4 MotionModels
4.1 Holonomic and Non-holonomic Systems
4.2 General Representation
4.3 Time Discrete Constant Turn Model
4.3.1 Basic Model
4.3.2 With Lateral Slip
4.3.3 With Timing Error
5 Contributions
5.1 Results
6 Discussion and Future Outlook
7 Summary of Papers

Author: Andersson, Lars

Source: Linköping University

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